Calibrating and validating with VISION (a quick view)

This is a quick exercise about how to use VISION to develop a Regression and to validate it with an independent test file.

I used in this case the Shootout files from 2002, where we have the same samples for calibration scanned in two instruments (same type), this files are CALIBRA1 (scanned in Instrument 1) and CALIBRA2 (scanned in instrument 2).

We have  another sample set for testing the calibration, TEST1 (test samples scanned in Instrument 1) and TEST2 (test samples scanned in Instrument 2). The idea of the Shootout exercise was to develop a calibration in Instrument 1 and transfer this calibration to Instrument 2, the best way possible.

In this exercise I just develop a PLS calibration with the CALIBRA1 and test it with TEST 1, but how this calibration performs with TEST2, we will see it in other post.

Shoot-out 2008_parte 006

Al igual que en las entradas previas, desarrollo la calibración con el software VISION, los tratamientos usados anteriormente no dan tan buenos estadísticos y en esta ocasión funciona mejor el tratamiento de 2ª derivada, para los mismos segmentos espectrales.

Same as previous posts, I have developed the calibration with another software (VISION), here the same math treatment an wavelengths regions as in the others, does not give the same statistics (a little bit worse), but the the 2º derivative gives a SEP similar.

Estadísticos de calibración / Calibration Statistics:

Para no tener overfitting VISION utiliza el estadístico PRESS, del que hablaremos proximamente:

To avoid overfitting VISION use the PRESS Statistic (We will talk about it soon)

Validamos con el conjunto de validación de la Campaña del 99, obteniendo:

Validation with the validation set.

El error de predicción SEP es de 0,1799.

The Standard Error of Prediction is: SEP = 0,1799

Visión nos dá los de Bias Pendiente e Intercepto y nos dice que en caso de ajuste el SEP bajaría a 0,1626. No obstante esto se debe ignorar.

Proximamente probaremos con Matlab y Unscrambrer para dar una evaluación general de los estadísticos de este parámetro.